Quality control metrics report (level 3): CD4_T
2021/03/15
1 Introduction
The purpose of this notebook is to plot several quality control metrics across clusters, so that we can have an idea of which clusters might be problematic.
1.1 Load packages
library(Seurat)
library(tidyverse)1.2 Parameters
# Paths
path_to_level_3 <- here::here("scRNA-seq/results/R_objects/level_3/")
path_to_level_3_cell_type <- str_c(path_to_level_3, cell_type, sep = "")
path_to_obj <- str_c(
path_to_level_3_cell_type,
"/",
cell_type,
"_clustered_level_3.rds",
sep = ""
)
# Functions
source(here::here("scRNA-seq/bin/utils.R"))
# Colors
color_palette <- c("black", "gray", "red", "yellow", "plum4", "green4",
"blue", "mediumorchid2", "coral2", "blueviolet",
"indianred4", "deepskyblue1", "dimgray", "deeppink1",
"green", "lightgray", "hotpink1", "gold", "brown",
"mediumvioletred", "mediumaquamarine")
# Point sizes
pt_sizes <- c(
NBC_MBC = 0.15,
GCBC = 0.15,
CD4_T = 0.25,
Cytotoxic = 0.4,
PC = 0.4,
myeloid = 0.6,
FDC = 0.6,
PDC = 1,
epithelial = 1
)
pt_size <- pt_sizes[cell_type]1.3 Load data
seurat <- readRDS(path_to_obj)
seurat## An object of class Seurat
## 37378 features across 78974 samples within 1 assay
## Active assay: RNA (37378 features, 0 variable features)
## 3 dimensional reductions calculated: pca, umap, harmony2 Visualize UMAP
umap_clusters <- DimPlot(
seurat,
group.by = "seurat_clusters",
pt.size = pt_size,
cols = color_palette
)
umap_clusters
3 Assess integration
p_assay <- plot_split_umap(seurat, var = "assay")
p_assay
4 pDNN and scrublet prediction
pDNN_vars <- c("pDNN_hashing", "pDNN_scrublet", "pDNN_union")
pDNN_gg <- purrr::map(pDNN_vars, function(x) {
p <- plot_pDNN(seurat_obj = seurat, pDNN_var = x, pt_size = pt_size)
p
})
pDNN_gg## [[1]]
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# Violin plots
pDNN_violins_gg <- purrr::map(pDNN_vars, function(x) {
p <- VlnPlot(
seurat,
features = x,
pt.size = 0,
group.by = "seurat_clusters",
cols = color_palette
) +
xlab("") +
theme(legend.position = "none")
p
})
pDNN_violins_gg## [[1]]
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# Scrublet
seurat$scrublet_predicted_doublet[seurat$scrublet_predicted_doublet == "True"] <- "TRUE"
seurat$scrublet_predicted_doublet[seurat$scrublet_predicted_doublet == "False"] <- "FALSE"
scrublet_gg <- DimPlot(seurat, group.by = "scrublet_predicted_doublet")
scrublet_gg
5 QC metrics
qc_vars <- c(
"nCount_RNA",
"nFeature_RNA",
"pct_mt",
"pct_ribosomal"
)
qc_gg <- purrr::map(qc_vars, function(x) {
p <- FeaturePlot(seurat, features = x, pt.size = pt_size)
p +
scale_color_viridis_c(option = "magma")
})
qc_gg## [[1]]
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# Violin plots
qc_violins_gg <- purrr::map(qc_vars, function(x) {
p <- VlnPlot(
seurat,
features = x,
pt.size = 0,
group.by = "seurat_clusters",
cols = color_palette
) +
xlab("") +
theme(legend.position = "none")
p
})
qc_violins_gg## [[1]]
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6 Cell cycle scoring
s_gg <- FeaturePlot(seurat, features = "S.Score", pt.size = pt_size) +
scale_color_viridis_c(option = "magma")
s_gg
g2m_gg <- FeaturePlot(seurat, features = "G2M.Score", pt.size = pt_size) +
scale_color_viridis_c(option = "magma")
g2m_gg
# Violin Plots
cc_violins_gg <- purrr::map(c("S.Score", "G2M.Score"), function(x) {
p <- VlnPlot(
seurat,
features = x,
pt.size = 0,
group.by = "seurat_clusters",
cols = color_palette
) +
xlab("") +
theme(legend.position = "none")
p
})
cc_violins_gg## [[1]]
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7 Session Information
sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server release 6.7 (Santiago)
##
## Matrix products: default
## BLAS: /apps/R/3.6.0/lib64/R/lib/libRblas.so
## LAPACK: /home/devel/rmassoni/anaconda3/lib/libmkl_rt.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=es_ES.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=es_ES.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=es_ES.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.4 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0 Seurat_3.2.0 BiocStyle_2.14.4
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_1.4-1 deldir_0.1-25 ellipsis_0.3.1 ggridges_0.5.2 rprojroot_1.3-2 fs_1.4.1 rstudioapi_0.11 spatstat.data_1.4-3 farver_2.0.3 leiden_0.3.3 listenv_0.8.0 ggrepel_0.8.2 fansi_0.4.1 lubridate_1.7.8 xml2_1.3.2 codetools_0.2-16 splines_3.6.0 knitr_1.28 polyclip_1.10-0 jsonlite_1.7.2 broom_0.5.6 ica_1.0-2 cluster_2.1.0 dbplyr_1.4.4 png_0.1-7 uwot_0.1.8 shiny_1.4.0.2 sctransform_0.2.1 BiocManager_1.30.10 compiler_3.6.0 httr_1.4.2 backports_1.1.7 assertthat_0.2.1 Matrix_1.2-18 fastmap_1.0.1 lazyeval_0.2.2 cli_2.0.2 later_1.0.0 htmltools_0.5.1.1 tools_3.6.0 rsvd_1.0.3 igraph_1.2.5 gtable_0.3.0 glue_1.4.1 RANN_2.6.1 reshape2_1.4.4 rappdirs_0.3.1 Rcpp_1.0.6 spatstat_1.64-1 cellranger_1.1.0 vctrs_0.3.6 ape_5.3 nlme_3.1-148
## [55] lmtest_0.9-37 xfun_0.14 globals_0.12.5 rvest_0.3.5 mime_0.9 miniUI_0.1.1.1 lifecycle_0.2.0 irlba_2.3.3 goftest_1.2-2 future_1.17.0 MASS_7.3-51.6 zoo_1.8-8 scales_1.1.1 hms_0.5.3 promises_1.1.0 spatstat.utils_1.17-0 parallel_3.6.0 RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.16 pbapply_1.4-2 gridExtra_2.3 rpart_4.1-15 stringi_1.4.6 rlang_0.4.10 pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-41 ROCR_1.0-11 tensor_1.5 labeling_0.3 patchwork_1.0.0 htmlwidgets_1.5.1 cowplot_1.0.0 tidyselect_1.1.0 here_0.1 RcppAnnoy_0.0.16 plyr_1.8.6 magrittr_1.5 bookdown_0.19 R6_2.4.1 generics_0.0.2 DBI_1.1.0 withr_2.4.1 pillar_1.4.4 haven_2.3.1 mgcv_1.8-31 fitdistrplus_1.1-1 survival_3.1-12 abind_1.4-5 future.apply_1.5.0 modelr_0.1.8 crayon_1.3.4 KernSmooth_2.23-17
## [109] plotly_4.9.2.1 rmarkdown_2.2 readxl_1.3.1 grid_3.6.0 data.table_1.12.8 blob_1.2.1 reprex_0.3.0 digest_0.6.20 xtable_1.8-4 httpuv_1.5.3.1 munsell_0.5.0 viridisLite_0.3.0